Multi-Objective Optimization Technology for Building Energy-Saving Renovation Strategy based on Genetic Algorithm

Building energy-saving design is significant for the industry to achieve carbon reduction and sustainable development. Firstly, a multi-objective model for energy consumption, cost, and carbon emissions is established based on the three-dimensional perspectives of society, nature, and economy. Then, a polynomial operator is used to improve the non dominated sorting genetic algorithm to calculate the optimal solution set. The low computational efficiency caused by direct coupling of algorithms in traditional optimization processes is expected to be addressed. Based on the results, the algorithm proposed in this study showed significant improvement in the reverse distance and convergence metrics for both the Square1 and Iris datasets, with an improvement of over 70% compared to the support vector machine-genetic algorithm and multi-objective clustering algorithm. The values obtained were closer to 0. The solution solved by this algorithm had lower building costs, energy consumption, and carbon emissions, with values of 345,200 yuan, 2,374 KWh/year, and 26 tons, respectively. This validates the effectiveness of the multi-objective model and solving algorithm in obtaining the optimal energy-saving design scheme for buildings. The results provide a reference for low-carbon optimization. © 2024 Regional Association for Security and crisis management. All rights reserved.